MMR-and-RMR-workflow-Clutch76

Load libraries (packages)

library("respR") ## respirometry/slope analysis
library("tidyverse") ## data manipulation

Set working directory

setwd("[PATH TO DIRECTORY]")

System1 - Dell

Importing data from firesting for resting

preexperiment_date <- "20 April 2023 11 09AM/All"
postexperiment_date <- "20 April 2023 04 08PM/All"

##--- last fish run in trial ---##
experiment_date <- "20 April 2023 12 31PM/Oxygen"
experiment_date2 <- "20 April 2023 12 31PM/All"

firesting <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19)

Cycle_1 <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

Cycle_last <-read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_date2,"slopes/Cycle_21.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

System2 - Asus

Importing data from firesting for resting

preexperiment_date_asus <- "20 April 2023 11 10AM/All"
postexperiment_date_asus <- "20 April 2023 04 48PM/All"

##--- last fish run in trial ---##
experiment_date_asus <- "20 April 2023 01 23PM/Oxygen"
experiment_date2_asus <- "20 April 2023 01 23PM/All"

firesting_asus <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19)

Cycle_1_asus <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

Cycle_last_asus <-read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date2_asus,"slopes/Cycle_21.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

Chamber volumes

chamber1_dell = 0.04650
chamber2_dell = 0.04593
chamber3_dell = 0.04977
chamber4_dell = 0.04860 

chamber1_asus = 0.04565
chamber2_asus = 0.04573
chamber3_asus = 0.04551
chamber4_asus = 0.04791

Date_tested="2023-04-20"
Clutch = "67" 
Male = "CSUD013" 
Female = "CSUD017"
Population = "Sudbury reef"
Tank =377 
salinity =36 
Date_analysed = Sys.Date() 

Replicates

1

Enter specimen data

Replicate = 1 
mass = 0.0009850
chamber = "ch4" 
Swim = "good/good"
chamber_vol = chamber4_dell
system1 = "Dell"
Notes="Second cycle used for max"

##--- time of trail ---## 
experiment_mmr_date <- "20 April 2023 12 09PM/Oxygen"
experiment_mmr_date2 <- "20 April 2023 12 09PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 

## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre

## [1] 0.001344838

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post2 <- post_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post3 <- post_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 

## [1] 0.0002634268

Resting metabolic rate

Data manipulation

firesting2 <- firesting |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.07 9.09
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME) 
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME) 
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])  

apoly_insp <- firesting2 |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)

## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  2  3  5  7  9 10 11 12 13 14 15 16 18 20 21 23 25 27 28 30
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.07 9.09
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=255, 
                              by="time", 
                              plot=TRUE) 

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...

## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates... 
## To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")

## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.

## adjust_rate: Rate adjustments applied using "linear" method.

apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)

## select_rate: Selecting rates with rsq values between 0.95 and 1...

## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----

## select_rate: Selecting lowest 6 *absolute* rate values...

## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:  13    1     360.4510 -0.02268875 0.990      NA 5878   6111 11507.52
## 2:  14    1     379.6548 -0.02323322 0.991      NA 6371   6605 12047.24
## 3:  16    1     409.6141 -0.02364688 0.997      NA 7360   7594 13127.58
## 4:  18    1     411.5254 -0.02193952 0.986      NA 8349   8582 14208.04
## 5:  19    1     459.5815 -0.02442111 0.993      NA 8842   9076 14747.19
## 6:  20    1     448.6791 -0.02285704 0.994      NA 9337   9571 15288.02
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 11762.75 98.939 93.114 -0.02268875 0.0005683348   -0.02325709 -0.02325709
## 2: 12302.92 99.232 93.463 -0.02323322 0.0005103217   -0.02374354 -0.02374354
## 3: 13383.01 98.971 92.936 -0.02364688 0.0003942604   -0.02404114 -0.02404114
## 4: 14462.85 99.436 93.850 -0.02193952 0.0002782061   -0.02221772 -0.02221772
## 5: 15002.69 99.203 92.913 -0.02442111 0.0002202413   -0.02464135 -0.02464135
## 6: 15543.50 98.946 93.089 -0.02285704 0.0001621341   -0.02301917 -0.02301917
##    oxy.unit time.unit volume     mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.0486 0.000985   NA 36 27 1.013253 -0.2641533
## 2:     %Air       sec 0.0486 0.000985   NA 36 27 1.013253 -0.2696785
## 3:     %Air       sec 0.0486 0.000985   NA 36 27 1.013253 -0.2730586
## 4:     %Air       sec 0.0486 0.000985   NA 36 27 1.013253 -0.2523482
## 5:     %Air       sec 0.0486 0.000985   NA 36 27 1.013253 -0.2798757
## 6:     %Air       sec 0.0486 0.000985   NA 36 27 1.013253 -0.2614511
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -268.1760          NA  mgO2/hr/kg   -268.1760
## 2:   -273.7852          NA  mgO2/hr/kg   -273.7852
## 3:   -277.2169          NA  mgO2/hr/kg   -277.2169
## 4:   -256.1911          NA  mgO2/hr/kg   -256.1911
## 5:   -284.1378          NA  mgO2/hr/kg   -284.1378
## 6:   -265.4326          NA  mgO2/hr/kg   -265.4326
## -----------------------------------------

## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest
67	1	CSUD013	CSUD017	Sudbury reef	377	0.000985	ch4	Dell	0.0486	2023-04-20	2024-06-20	good/good	36	27	273.7497	0.2696435	0.993

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.08 1.67
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start

## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 

## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 

## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.

inspect(cycle1_data)

## inspect: Applying column default of 'time = 1'

## inspect: Applying column default of 'oxygen = 2'

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  4  5  7  8  9 11 12 13 14 15 17 18 20 22 23 24 25 26
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.08 1.63
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()

## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     401.4706 -0.08234930 0.9896936      NA  79    132 3753.67
##   2:  NA    2     401.4300 -0.08233857 0.9896819      NA  78    131 3752.57
##   3:  NA    3     401.2129 -0.08228126 0.9896904      NA  80    133 3754.76
##   4:  NA    4     401.1390 -0.08226162 0.9896535      NA  77    130 3751.42
##   5:  NA    5     400.8464 -0.08218407 0.9896345      NA  76    129 3750.33
##  ---                                                                       
## 211:  NA  211     286.1124 -0.05239942 0.9925774      NA 201    254 3890.67
## 212:  NA  212     285.8174 -0.05231741 0.9928004      NA 197    250 3886.19
## 213:  NA  213     285.2897 -0.05218817 0.9932634      NA 200    253 3889.51
## 214:  NA  214     285.1300 -0.05214365 0.9934978      NA 198    251 3887.33
## 215:  NA  215     284.9593 -0.05210199 0.9936736      NA 199    252 3888.42
##      endtime    oxy endoxy        rate
##   1: 3813.67 92.354 87.416 -0.08234930
##   2: 3812.57 92.420 87.486 -0.08233857
##   3: 3814.76 92.304 87.340 -0.08228126
##   4: 3811.42 92.494 87.569 -0.08226162
##   5: 3810.33 92.595 87.635 -0.08218407
##  ---                                  
## 211: 3950.67 82.257 78.901 -0.05239942
## 212: 3946.19 82.710 79.267 -0.05231741
## 213: 3949.51 82.375 78.953 -0.05218817
## 214: 3947.33 82.627 79.168 -0.05214365
## 215: 3948.42 82.525 79.050 -0.05210199
## 
## Regressions : 215 | Results : 215 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj

## adjust_rate: Rate adjustments applied using "mean" method.

## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 215 adjusted rate(s):
## Rate          : -0.0823493
## Adjustment    : 0.001344838
## Adjusted Rate : -0.08369414 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)

## select_rate: Selecting rates with rsq values between 0.93 and 1...

## ----- Selection complete. 0 rate(s) removed, 215 rate(s) remaining -----

## select_rate: Selecting highest 1 *absolute* rate values...

## ----- Selection complete. 214 rate(s) removed, 1 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0   slope_b1       rsq density row endrow    time
## 1:  NA    1     401.4706 -0.0823493 0.9896936      NA  79    132 3753.67
##    endtime    oxy endoxy       rate  adjustment rate.adjusted  rate.input
## 1: 3813.67 92.354 87.416 -0.0823493 0.001344838   -0.08369414 -0.08369414
##    oxy.unit time.unit volume     mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.0486 0.000985   NA 36 27 1.013253 -0.9505956
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -965.0717          NA  mgO2/hr/kg   -965.0717
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass, 
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
                      True_resting="") 
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest	Max_kg	Max	rsqmax	AAS_kg	AAS	Notes	True_resting
67	1	CSUD013	CSUD017	Sudbury reef	377	0.000985	ch4	Dell	0.0486	2023-04-20	2024-06-20	good/good	36	27	273.7497	0.2696435	0.993	965.0717	0.9505956	0.9896936	691.322	0.6809522	Second cycle used for max

Exporting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 

## Rows: 208 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 

write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

2

Enter specimen data

Replicate = 2 
mass = 0.0007459 
chamber = "ch3" 
Swim = "good/good"
chamber_vol = chamber3_dell
system1 = "Dell"
Notes="Second cycle used for max"

##--- time of trail ---## 
experiment_mmr_date <- "20 April 2023 12 19PM/Oxygen"
experiment_mmr_date2 <- "20 April 2023 12 19PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 

## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre

## [1] 0.0007834151

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post2 <- post_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post3 <- post_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 

## [1] -8.226355e-05

Resting metabolic rate

Data manipulation

firesting2 <- firesting |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.07 9.09
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME) 
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME) 
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])  

apoly_insp <- firesting2 |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)

## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  2  3  5  7  9 10 11 12 13 14 15 16 18 20 21 23 25 27 28 30
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.07 9.09
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=255, 
                              by="time", 
                              plot=TRUE) 

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...

## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates... 
## To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")

## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.

## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve). 
## Ensure this is correct. The 'linear' adjustment has been performed regardless.

## adjust_rate: Rate adjustments applied using "linear" method.

apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)

## select_rate: Selecting rates with rsq values between 0.95 and 1...

## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----

## select_rate: Selecting lowest 6 *absolute* rate values...

## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:   5    1     227.7643 -0.01790075 0.978      NA 1952   2184  7187.53
## 2:  10    1     301.6079 -0.02059416 0.997      NA 4400   4633  9887.72
## 3:  11    1     311.6680 -0.02041892 0.979      NA 4892   5125 10427.44
## 4:  13    1     338.7034 -0.02088923 0.995      NA 5878   6111 11507.52
## 5:  17    1     397.4166 -0.02184062 0.996      NA 7854   8087 13667.49
## 6:  18    1     409.3491 -0.02184339 0.991      NA 8349   8582 14208.04
##     endtime    oxy endoxy        rate    adjustment rate.adjusted  rate.input
## 1:  7442.90 99.002 94.150 -0.01790075  5.333690e-04   -0.01843412 -0.01843412
## 2: 10143.57 98.038 92.471 -0.02059416  3.011082e-04   -0.02089527 -0.02089527
## 3: 10682.98 98.433 93.232 -0.02041892  2.547009e-04   -0.02067362 -0.02067362
## 4: 11762.75 98.116 92.757 -0.02088923  1.618178e-04   -0.02105105 -0.02105105
## 5: 13922.29 98.722 93.130 -0.02184062 -2.394012e-05   -0.02181668 -0.02181668
## 6: 14462.85 98.743 93.117 -0.02184339 -7.043261e-05   -0.02177296 -0.02177296
##    oxy.unit time.unit  volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04977 0.0007459   NA 36 27 1.013253 -0.2144147
## 2:     %Air       sec 0.04977 0.0007459   NA 36 27 1.013253 -0.2430413
## 3:     %Air       sec 0.04977 0.0007459   NA 36 27 1.013253 -0.2404632
## 4:     %Air       sec 0.04977 0.0007459   NA 36 27 1.013253 -0.2448532
## 5:     %Air       sec 0.04977 0.0007459   NA 36 27 1.013253 -0.2537586
## 6:     %Air       sec 0.04977 0.0007459   NA 36 27 1.013253 -0.2532501
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -287.4576          NA  mgO2/hr/kg   -287.4576
## 2:   -325.8363          NA  mgO2/hr/kg   -325.8363
## 3:   -322.3800          NA  mgO2/hr/kg   -322.3800
## 4:   -328.2655          NA  mgO2/hr/kg   -328.2655
## 5:   -340.2046          NA  mgO2/hr/kg   -340.2046
## 6:   -339.5228          NA  mgO2/hr/kg   -339.5228
## -----------------------------------------

## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest
67	2	CSUD013	CSUD017	Sudbury reef	377	0.0007459	ch3	Dell	0.04977	2023-04-20	2024-06-20	good/good	36	27	331.2419	0.2470733	0.9916

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.08 1.67
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start

## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 

## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row,  
              by = "row") 

## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.

inspect(cycle1_data)

## inspect: Applying column default of 'time = 1'

## inspect: Applying column default of 'oxygen = 2'

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  6  7 10 12 13 14 15 17 18 19 21 22 23 24 25 26
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.08 1.55
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()

## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     376.5332 -0.06387502 0.9940789      NA 125    178 4419.30
##   2:  NA    2     376.4767 -0.06386188 0.9940718      NA 126    179 4420.42
##   3:  NA    3     376.0997 -0.06377806 0.9939653      NA 124    177 4418.22
##   4:  NA    4     375.9788 -0.06374946 0.9939357      NA 127    180 4421.52
##   5:  NA    5     375.1913 -0.06357411 0.9938111      NA 123    176 4417.13
##  ---                                                                       
## 206:  NA  206     231.3930 -0.03081946 0.9896539      NA   5     58 4281.19
## 207:  NA  207     230.5701 -0.03062889 0.9890660      NA   4     57 4280.04
## 208:  NA  208     230.1059 -0.03052145 0.9887659      NA   3     56 4278.95
## 209:  NA  209     229.0772 -0.03028324 0.9875741      NA   2     55 4277.86
## 210:  NA  210     227.7603 -0.02997790 0.9863572      NA   1     54 4276.74
##      endtime    oxy endoxy        rate
##   1: 4479.30 94.260 90.448 -0.06387502
##   2: 4480.42 94.185 90.401 -0.06386188
##   3: 4478.22 94.270 90.470 -0.06377806
##   4: 4481.52 94.153 90.376 -0.06374946
##   5: 4477.13 94.308 90.640 -0.06357411
##  ---                                  
## 206: 4341.19 99.364 97.586 -0.03081946
## 207: 4340.04 99.421 97.642 -0.03062889
## 208: 4338.95 99.383 97.674 -0.03052145
## 209: 4337.86 99.406 97.679 -0.03028324
## 210: 4336.74 99.416 97.678 -0.02997790
## 
## Regressions : 210 | Results : 210 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj

## adjust_rate: Rate adjustments applied using "mean" method.

## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 210 adjusted rate(s):
## Rate          : -0.06387502
## Adjustment    : 0.0007834151
## Adjusted Rate : -0.06465843 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)

## select_rate: Selecting rates with rsq values between 0.93 and 1...

## ----- Selection complete. 0 rate(s) removed, 210 rate(s) remaining -----

## select_rate: Selecting highest 1 *absolute* rate values...

## ----- Selection complete. 209 rate(s) removed, 1 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow   time
## 1:  NA    1     376.5332 -0.06387502 0.9940789      NA 125    178 4419.3
##    endtime   oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1:  4479.3 94.26 90.448 -0.06387502 0.0007834151   -0.06465843 -0.06465843
##    oxy.unit time.unit  volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04977 0.0007459   NA 36 27 1.013253 -0.7520683
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:    -1008.27          NA  mgO2/hr/kg    -1008.27
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass, 
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
                      Notes=Notes, 
                      True_resting="") 
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest	Max_kg	Max	rsqmax	AAS_kg	AAS	Notes	True_resting
67	2	CSUD013	CSUD017	Sudbury reef	377	0.0007459	ch3	Dell	0.04977	2023-04-20	2024-06-20	good/good	36	27	331.2419	0.2470733	0.9916	1008.27	0.7520683	0.9940789	677.0278	0.504995	Second cycle used for max

Exporting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 

## Rows: 209 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 

write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

3

Enter specimen data

Replicate = 3 
mass = 0.0005618 
chamber = "ch2" 
Swim = "good/good"
chamber_vol = chamber2_dell
system1 = "Dell"
Notes=""

##--- time of trail ---## 
experiment_mmr_date <- "20 April 2023 12 19PM/Oxygen"
experiment_mmr_date2 <- "20 April 2023 12 19PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 

## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre

## [1] 0.0008175449

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post2 <- post_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post3 <- post_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 

## [1] 8.090708e-05

Resting metabolic rate

Data manipulation

firesting2 <- firesting |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.07 9.09
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME) 
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME) 
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])  

apoly_insp <- firesting2 |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)

## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  2  3  5  7  9 10 11 12 13 14 15 16 18 20 21 23 25 27 28 30
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.07 9.09
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=255, 
                              by="time", 
                              plot=TRUE) 

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...

## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates... 
## To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")

## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.

## adjust_rate: Rate adjustments applied using "linear" method.

apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)

## select_rate: Selecting rates with rsq values between 0.95 and 1...

## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----

## select_rate: Selecting lowest 6 *absolute* rate values...

## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:  13    1     233.6031 -0.01170060 0.992      NA 5878   6111 11507.52
## 2:  14    1     272.2122 -0.01435249 0.980      NA 6371   6605 12047.24
## 3:  18    1     319.8553 -0.01552306 0.984      NA 8349   8582 14208.04
## 4:  19    1     324.4065 -0.01528727 0.988      NA 8842   9076 14747.19
## 5:  20    1     340.5416 -0.01580339 0.984      NA 9337   9571 15288.02
## 6:  21    1     356.6614 -0.01625997 0.992      NA 9831  10064 15827.65
##     endtime    oxy endoxy        rate    adjustment rate.adjusted  rate.input
## 1: 11762.75 98.897 95.775 -0.01170060  2.886049e-04   -0.01198921 -0.01198921
## 2: 12302.92 99.233 95.235 -0.01435249  2.490874e-04   -0.01460157 -0.01460157
## 3: 14462.85 99.160 94.864 -0.01552306  9.097447e-05   -0.01561404 -0.01561404
## 4: 15002.69 98.861 94.599 -0.01528727  5.148987e-05   -0.01533876 -0.01533876
## 5: 15543.50 98.879 94.654 -0.01580339  1.190831e-05   -0.01581530 -0.01581530
## 6: 16082.30 99.330 95.299 -0.01625997 -2.755580e-05   -0.01623241 -0.01623241
##    oxy.unit time.unit  volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04593 0.0005618   NA 36 27 1.013253 -0.1286920
## 2:     %Air       sec 0.04593 0.0005618   NA 36 27 1.013253 -0.1567331
## 3:     %Air       sec 0.04593 0.0005618   NA 36 27 1.013253 -0.1676008
## 4:     %Air       sec 0.04593 0.0005618   NA 36 27 1.013253 -0.1646460
## 5:     %Air       sec 0.04593 0.0005618   NA 36 27 1.013253 -0.1697612
## 6:     %Air       sec 0.04593 0.0005618   NA 36 27 1.013253 -0.1742384
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -229.0708          NA  mgO2/hr/kg   -229.0708
## 2:   -278.9837          NA  mgO2/hr/kg   -278.9837
## 3:   -298.3282          NA  mgO2/hr/kg   -298.3282
## 4:   -293.0686          NA  mgO2/hr/kg   -293.0686
## 5:   -302.1737          NA  mgO2/hr/kg   -302.1737
## 6:   -310.1432          NA  mgO2/hr/kg   -310.1432
## -----------------------------------------

## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple")  

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest
67	3	CSUD013	CSUD017	Sudbury reef	377	0.0005618	ch2	Dell	0.04593	2023-04-20	2024-06-20	good/good	36	27	296.5395	0.1665959	0.9856

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.08 1.67
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start

## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 

## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row+150, # custom
              to = cycle1.end.row, 
              by = "row") 

## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.

inspect(cycle1_data)

## inspect: Applying column default of 'time = 1'

## inspect: Applying column default of 'oxygen = 2'

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 20 23
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.08 1.55
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()

## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##     rep rank intercept_b0    slope_b1       rsq density row endrow    time
##  1:  NA    1     289.7578 -0.04390422 0.9940759      NA   2     54 4448.90
##  2:  NA    2     289.2865 -0.04379875 0.9938077      NA   1     53 4447.79
##  3:  NA    3     289.2288 -0.04378675 0.9938896      NA   3     55 4450.06
##  4:  NA    4     288.6545 -0.04365852 0.9937960      NA   4     56 4451.15
##  5:  NA    5     288.0963 -0.04353338 0.9934534      NA   5     57 4452.50
## ---                                                                       
## 57:  NA   57     271.2739 -0.03979179 0.9862865      NA  42     94 4494.65
## 58:  NA   58     271.0784 -0.03974794 0.9860808      NA  38     90 4490.22
## 59:  NA   59     270.8169 -0.03969077 0.9862323      NA  41     93 4493.53
## 60:  NA   60     270.7139 -0.03966792 0.9862340      NA  40     92 4492.44
## 61:  NA   61     270.6954 -0.03966376 0.9862384      NA  39     91 4491.35
##     endtime    oxy endoxy        rate
##  1: 4508.90 94.517 91.744 -0.04390422
##  2: 4507.79 94.533 91.775 -0.04379875
##  3: 4510.06 94.393 91.705 -0.04378675
##  4: 4511.15 94.349 91.746 -0.04365852
##  5: 4512.50 94.228 91.751 -0.04353338
## ---                                  
## 57: 4554.65 92.362 90.010 -0.03979179
## 58: 4550.22 92.670 90.209 -0.03974794
## 59: 4553.53 92.424 90.068 -0.03969077
## 60: 4552.44 92.496 90.119 -0.03966792
## 61: 4551.35 92.545 90.131 -0.03966376
## 
## Regressions : 61 | Results : 61 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj

## adjust_rate: Rate adjustments applied using "mean" method.

## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 61 adjusted rate(s):
## Rate          : -0.04390422
## Adjustment    : 0.0008175449
## Adjusted Rate : -0.04472176 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)

## select_rate: Selecting rates with rsq values between 0.93 and 1...

## ----- Selection complete. 0 rate(s) removed, 61 rate(s) remaining -----

## select_rate: Selecting highest 1 *absolute* rate values...

## ----- Selection complete. 60 rate(s) removed, 1 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow   time
## 1:  NA    1     289.7578 -0.04390422 0.9940759      NA   2     54 4448.9
##    endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1:  4508.9 94.517 91.744 -0.04390422 0.0008175449   -0.04472176 -0.04472176
##    oxy.unit time.unit  volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04593 0.0005618   NA 36 27 1.013253 -0.4800427
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -854.4725          NA  mgO2/hr/kg   -854.4725
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass, 
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
                      Notes=Notes, 
                      True_resting="") 
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest	Max_kg	Max	rsqmax	AAS_kg	AAS	Notes	True_resting
67	3	CSUD013	CSUD017	Sudbury reef	377	0.0005618	ch2	Dell	0.04593	2023-04-20	2024-06-20	good/good	36	27	296.5395	0.1665959	0.9856	854.4725	0.4800427	0.9940759	557.933	0.3134468

Exporting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 

## Rows: 210 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 

write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

4

Enter specimen data

Replicate = 4 
mass = 0.0007015
chamber = "ch1" 
Swim = "good/good"
chamber_vol = chamber1_dell
system1 = "Dell"
Notes=""

##--- time of trail ---## 
experiment_mmr_date <- "20 April 2023 12 31PM/Oxygen"
experiment_mmr_date2 <- "20 April 2023 12 31PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 

## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre

## [1] 0.000339042

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post2 <- post_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post3 <- post_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 

## [1] -0.001849456

Resting metabolic rate

Data manipulation

firesting2 <- firesting |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.07 9.09
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME) 
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME) 
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])  

apoly_insp <- firesting2 |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)

## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  2  3  5  7  9 10 11 12 13 14 15 16 18 20 21 23 25 27 28 30
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.07 9.09
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=255, 
                              by="time", 
                              plot=TRUE)  

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...

## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates... 
## To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")

## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.

## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve). 
## Ensure this is correct. The 'linear' adjustment has been performed regardless.

## adjust_rate: Rate adjustments applied using "linear" method.

apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)

## select_rate: Selecting rates with rsq values between 0.95 and 1...

## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----

## select_rate: Selecting lowest 6 *absolute* rate values...

## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:  16    1     473.3257 -0.02863468 0.999      NA 7360   7594 13127.58
## 2:  17    1     491.0381 -0.02875783 0.996      NA 7854   8087 13667.49
## 3:  18    1     517.2371 -0.02949486 0.999      NA 8349   8582 14208.04
## 4:  19    1     527.2155 -0.02911755 0.998      NA 8842   9076 14747.19
## 5:  20    1     512.8128 -0.02715079 0.996      NA 9337   9571 15288.02
## 6:  21    1     546.6795 -0.02834704 0.996      NA 9831  10064 15827.65
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 13383.01 97.264 90.186 -0.02863468 -0.001584682   -0.02705000 -0.02705000
## 2: 13922.29 98.036 90.586 -0.02875783 -0.001702010   -0.02705582 -0.02705582
## 3: 14462.85 98.079 90.606 -0.02949486 -0.001819546   -0.02767531 -0.02767531
## 4: 15002.69 97.847 90.490 -0.02911755 -0.001936852   -0.02718069 -0.02718069
## 5: 15543.50 97.641 90.805 -0.02715079 -0.002054446   -0.02509634 -0.02509634
## 6: 16082.30 98.000 91.049 -0.02834704 -0.002171691   -0.02617535 -0.02617535
##    oxy.unit time.unit volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.0465 0.0007015   NA 36 27 1.013253 -0.2939576
## 2:     %Air       sec 0.0465 0.0007015   NA 36 27 1.013253 -0.2940209
## 3:     %Air       sec 0.0465 0.0007015   NA 36 27 1.013253 -0.3007530
## 4:     %Air       sec 0.0465 0.0007015   NA 36 27 1.013253 -0.2953779
## 5:     %Air       sec 0.0465 0.0007015   NA 36 27 1.013253 -0.2727268
## 6:     %Air       sec 0.0465 0.0007015   NA 36 27 1.013253 -0.2844526
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -419.0415          NA  mgO2/hr/kg   -419.0415
## 2:   -419.1317          NA  mgO2/hr/kg   -419.1317
## 3:   -428.7284          NA  mgO2/hr/kg   -428.7284
## 4:   -421.0661          NA  mgO2/hr/kg   -421.0661
## 5:   -388.7766          NA  mgO2/hr/kg   -388.7766
## 6:   -405.4920          NA  mgO2/hr/kg   -405.4920
## -----------------------------------------

## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple")  

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest
67	4	CSUD013	CSUD017	Sudbury reef	377	0.0007015	ch1	Dell	0.0465	2023-04-20	2024-06-20	good/good	36	27	418.6919	0.2937124	0.9976

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.07 9.09
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start

## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 

## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 

## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.

inspect(cycle1_data)

## inspect: Applying column default of 'time = 1'

## inspect: Applying column default of 'oxygen = 2'

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  2  3  5  7  9 10 11 12 13 14 15 16 18 20 21 23 25 27 28 30
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.08 1.35
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()

## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     544.4877 -0.08929536 0.9972643      NA  16     69 5000.68
##   2:  NA    2     544.4216 -0.08928317 0.9972522      NA  15     68 4999.58
##   3:  NA    3     544.2933 -0.08925857 0.9972260      NA  14     67 4998.43
##   4:  NA    4     543.9045 -0.08918210 0.9971494      NA  13     66 4997.32
##   5:  NA    5     543.6907 -0.08913575 0.9970435      NA  17     70 5002.01
##  ---                                                                       
## 206:  NA  206     274.1214 -0.03609821 0.9729605      NA 149    202 5153.42
## 207:  NA  207     273.7053 -0.03601755 0.9730039      NA 148    201 5152.08
## 208:  NA  208     273.6156 -0.03599958 0.9730201      NA 145    198 5148.74
## 209:  NA  209     273.3221 -0.03594314 0.9730264      NA 146    199 5149.83
## 210:  NA  210     273.3043 -0.03593992 0.9730276      NA 147    200 5150.99
##      endtime    oxy endoxy        rate
##   1: 5060.68 97.873 92.714 -0.08929536
##   2: 5059.58 97.969 92.799 -0.08928317
##   3: 5058.43 98.057 92.877 -0.08925857
##   4: 5057.32 98.165 92.940 -0.08918210
##   5: 5062.01 97.783 92.684 -0.08913575
##  ---                                  
## 206: 5213.42 88.139 85.858 -0.03609821
## 207: 5212.08 88.171 85.926 -0.03601755
## 208: 5208.74 88.364 86.114 -0.03599958
## 209: 5209.83 88.285 86.048 -0.03594314
## 210: 5210.99 88.229 85.992 -0.03593992
## 
## Regressions : 210 | Results : 210 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj

## adjust_rate: Rate adjustments applied using "mean" method.

## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 210 adjusted rate(s):
## Rate          : -0.08929536
## Adjustment    : 0.000339042
## Adjusted Rate : -0.0896344 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)

## select_rate: Selecting rates with rsq values between 0.93 and 1...

## ----- Selection complete. 0 rate(s) removed, 210 rate(s) remaining -----

## select_rate: Selecting highest 1 *absolute* rate values...

## ----- Selection complete. 209 rate(s) removed, 1 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1     544.4877 -0.08929536 0.9972643      NA  16     69 5000.68
##    endtime    oxy endoxy        rate  adjustment rate.adjusted rate.input
## 1: 5060.68 97.873 92.714 -0.08929536 0.000339042    -0.0896344 -0.0896344
##    oxy.unit time.unit volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.0465 0.0007015   NA 36 27 1.013253 -0.9740744
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -1388.559          NA  mgO2/hr/kg   -1388.559
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass, 
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
                      Notes=Notes, 
                      True_resting="") 
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest	Max_kg	Max	rsqmax	AAS_kg	AAS	Notes	True_resting
67	4	CSUD013	CSUD017	Sudbury reef	377	0.0007015	ch1	Dell	0.0465	2023-04-20	2024-06-20	good/good	36	27	418.6919	0.2937124	0.9976	1388.559	0.9740744	0.9972643	969.8675	0.680362

Exporting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 

## Rows: 211 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 

write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

5

Enter specimen data

Replicate = 5 
mass = 0.0007987 
chamber = "ch4" 
Swim = "good/good"
chamber_vol = chamber4_asus
system1 = "Asus"
Notes="not reliable?"

##--- time of trail ---## 
experiment_mmr_date_asus <- "20 April 2023 12 48PM/Oxygen"
experiment_mmr_date2_asus <- "20 April 2023 12 48PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 

## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre

## [1] -0.00119473

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post2 <- post_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post3 <- post_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 

## [1] -0.002457498

Resting metabolic rate

Data manipulation

firesting2_asus <- firesting_asus |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_asus, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.34 3.77
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME) 
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME) 
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])  

apoly_insp <- firesting2_asus |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)

## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 21
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.34 1.66
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=255, 
                              by="time", 
                              plot=TRUE) 

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...

## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")

## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.

## adjust_rate: Rate adjustments applied using "linear" method.

apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)

## select_rate: Selecting rates with rsq values between 0.95 and 1...

## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----

## select_rate: Selecting lowest 6 *absolute* rate values...

## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:   3    1     273.3595 -0.01898376 0.981      NA  820   1006  9208.09
## 2:  11    1     353.9710 -0.01894255 0.991      NA 3967   4153 13527.06
## 3:  13    1     363.2070 -0.01812522 0.973      NA 4756   4942 14608.21
## 4:  15    1     409.0459 -0.01982182 0.992      NA 5543   5729 15687.34
## 5:  16    1     415.9753 -0.01956546 0.983      NA 5938   6124 16228.35
## 6:  17    1     403.5047 -0.01818126 0.963      NA 6332   6518 16767.97
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1:  9463.31 98.288 93.474 -0.01898376 -0.001734242   -0.01724951 -0.01724951
## 2: 13781.54 97.628 92.694 -0.01894255 -0.002408082   -0.01653447 -0.01653447
## 3: 14863.51 98.450 93.578 -0.01812522 -0.002576840   -0.01554838 -0.01554838
## 4: 15942.14 97.993 92.958 -0.01982182 -0.002745180   -0.01707664 -0.01707664
## 5: 16483.17 98.137 93.501 -0.01956546 -0.002829597   -0.01673586 -0.01673586
## 6: 17023.06 98.638 93.449 -0.01818126 -0.002913816   -0.01526745 -0.01526745
##    oxy.unit time.unit  volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04791 0.0007987   NA 36 27 1.013253 -0.1931379
## 2:     %Air       sec 0.04791 0.0007987   NA 36 27 1.013253 -0.1851317
## 3:     %Air       sec 0.04791 0.0007987   NA 36 27 1.013253 -0.1740908
## 4:     %Air       sec 0.04791 0.0007987   NA 36 27 1.013253 -0.1912022
## 5:     %Air       sec 0.04791 0.0007987   NA 36 27 1.013253 -0.1873867
## 6:     %Air       sec 0.04791 0.0007987   NA 36 27 1.013253 -0.1709453
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -241.8153          NA  mgO2/hr/kg   -241.8153
## 2:   -231.7913          NA  mgO2/hr/kg   -231.7913
## 3:   -217.9677          NA  mgO2/hr/kg   -217.9677
## 4:   -239.3918          NA  mgO2/hr/kg   -239.3918
## 5:   -234.6146          NA  mgO2/hr/kg   -234.6146
## 6:   -214.0294          NA  mgO2/hr/kg   -214.0294
## -----------------------------------------

## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest
67	5	CSUD013	CSUD017	Sudbury reef	377	0.0007987	ch4	Asus	0.04791	2023-04-20	2024-06-20	good/good	36	27	233.1162	0.1861899	0.984

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.34 1.85
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start

## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 

## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 

## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.

inspect(cycle1_data)

## inspect: Applying column default of 'time = 1'

## inspect: Applying column default of 'oxygen = 2'

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  3  5  6  7  8 10 11 12 13 14 15 16 17 18 19 20 21 23 24
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.34 1.68
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()

## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     441.2738 -0.05688400 0.9894732      NA 112    156 6097.43
##   2:  NA    2     440.6578 -0.05678386 0.9893376      NA 111    155 6096.08
##   3:  NA    3     440.3219 -0.05672803 0.9891323      NA 113    157 6098.80
##   4:  NA    4     438.4006 -0.05641572 0.9888804      NA 110    154 6094.73
##   5:  NA    5     437.2645 -0.05622836 0.9876496      NA 114    158 6100.17
##  ---                                                                       
## 171:  NA  171     250.0906 -0.02548004 0.9359655      NA  31     75 5986.49
## 172:  NA  172     248.4899 -0.02521511 0.9380592      NA  32     76 5987.84
## 173:  NA  173     247.7540 -0.02509255 0.9406762      NA  35     79 5991.99
## 174:  NA  174     246.6502 -0.02490983 0.9380275      NA  33     77 5989.24
## 175:  NA  175     245.6425 -0.02474237 0.9370798      NA  34     78 5990.61
##      endtime    oxy endoxy        rate
##   1: 6157.43 94.368 91.044 -0.05688400
##   2: 6156.08 94.386 91.058 -0.05678386
##   3: 6158.80 94.312 91.064 -0.05672803
##   4: 6154.73 94.476 91.156 -0.05641572
##   5: 6160.17 94.269 91.114 -0.05622836
##  ---                                  
## 171: 6046.49 97.693 96.086 -0.02548004
## 172: 6047.84 97.721 95.911 -0.02521511
## 173: 6051.99 97.265 95.881 -0.02509255
## 174: 6049.24 97.591 95.980 -0.02490983
## 175: 6050.61 97.460 95.980 -0.02474237
## 
## Regressions : 175 | Results : 175 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj

## adjust_rate: Rate adjustments applied using "mean" method.

## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 175 adjusted rate(s):
## Rate          : -0.056884
## Adjustment    : -0.00119473
## Adjusted Rate : -0.05568927 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)

## select_rate: Selecting rates with rsq values between 0.93 and 1...

## ----- Selection complete. 17 rate(s) removed, 158 rate(s) remaining -----

## select_rate: Selecting highest 1 *absolute* rate values...

## ----- Selection complete. 157 rate(s) removed, 1 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0  slope_b1       rsq density row endrow    time endtime
## 1:  NA    1     441.2738 -0.056884 0.9894732      NA 112    156 6097.43 6157.43
##       oxy endoxy      rate  adjustment rate.adjusted  rate.input oxy.unit
## 1: 94.368 91.044 -0.056884 -0.00119473   -0.05568927 -0.05568927     %Air
##    time.unit  volume      mass area  S  t        P   rate.abs rate.m.spec
## 1:       sec 0.04791 0.0007987   NA 36 27 1.013253 -0.6235369   -780.6898
##    rate.a.spec output.unit rate.output
## 1:          NA  mgO2/hr/kg   -780.6898
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass,
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
         True_resting="") 
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest	Max_kg	Max	rsqmax	AAS_kg	AAS	Notes	True_resting
67	5	CSUD013	CSUD017	Sudbury reef	377	0.0007987	ch4	Asus	0.04791	2023-04-20	2024-06-20	good/good	36	27	233.1162	0.1861899	0.984	780.6898	0.6235369	0.9894732	547.5736	0.4373471	not reliable?
### Expor	ting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 

## Rows: 212 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 

write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

6

Enter specimen data

Replicate = 6 
mass = 0.0003786 
chamber = "ch3" 
Swim = "good/good"
chamber_vol = chamber3_asus
system1 = "Asus"
Notes=""

##--- time of trail ---## 
experiment_mmr_date_asus <- "20 April 2023 01 01PM/Oxygen"
experiment_mmr_date2_asus <- "20 April 2023 01 01PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 

## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre

## [1] -0.002117048

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post2 <- post_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post3 <- post_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 

## [1] -0.003262535

Resting metabolic rate

Data manipulation

firesting2_asus <- firesting_asus |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_asus, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.34 3.77
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME) 
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME) 
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])  

apoly_insp <- firesting2_asus |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)

## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 21
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.34 1.66
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=245, 
                              by="time", 
                              plot=TRUE) 

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...

## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")

## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.

## adjust_rate: Rate adjustments applied using "linear" method.

apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)

## select_rate: Selecting rates with rsq values between 0.95 and 1...

## ----- Selection complete. 1 rate(s) removed, 20 rate(s) remaining -----

## select_rate: Selecting lowest 6 *absolute* rate values...

## ----- Selection complete. 14 rate(s) removed, 6 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:   4    1     284.4268 -0.01907199 0.996      NA 1213   1390  9748.26
## 2:   9    1     339.1099 -0.01936956 0.996      NA 3180   3359 12448.29
## 3:  11    1     372.2013 -0.02028354 0.992      NA 3967   4146 13527.06
## 4:  14    1     387.2775 -0.01912618 0.992      NA 5149   5328 15147.02
## 5:  17    1     446.1266 -0.02075712 0.990      NA 6332   6511 16767.97
## 6:  18    1     441.6263 -0.01986688 0.996      NA 6726   6905 17307.62
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1:  9992.50 98.593 93.819 -0.01907199 -0.002682131   -0.01638985 -0.01638985
## 2: 12693.69 98.253 93.100 -0.01936956 -0.003064377   -0.01630518 -0.01630518
## 3: 13771.98 97.628 92.955 -0.02028354 -0.003217033   -0.01706651 -0.01706651
## 4: 15392.34 97.716 92.794 -0.01912618 -0.003446351   -0.01567983 -0.01567983
## 5: 17013.43 98.159 92.709 -0.02075712 -0.003675791   -0.01708133 -0.01708133
## 6: 17552.71 97.710 92.871 -0.01986688 -0.003752147   -0.01611473 -0.01611473
##    oxy.unit time.unit  volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04551 0.0003786   NA 36 27 1.013253 -0.1743197
## 2:     %Air       sec 0.04551 0.0003786   NA 36 27 1.013253 -0.1734191
## 3:     %Air       sec 0.04551 0.0003786   NA 36 27 1.013253 -0.1815165
## 4:     %Air       sec 0.04551 0.0003786   NA 36 27 1.013253 -0.1667680
## 5:     %Air       sec 0.04551 0.0003786   NA 36 27 1.013253 -0.1816741
## 6:     %Air       sec 0.04551 0.0003786   NA 36 27 1.013253 -0.1713935
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -460.4323          NA  mgO2/hr/kg   -460.4323
## 2:   -458.0536          NA  mgO2/hr/kg   -458.0536
## 3:   -479.4412          NA  mgO2/hr/kg   -479.4412
## 4:   -440.4860          NA  mgO2/hr/kg   -440.4860
## 5:   -479.8575          NA  mgO2/hr/kg   -479.8575
## 6:   -452.7035          NA  mgO2/hr/kg   -452.7035
## -----------------------------------------

## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest
67	6	CSUD013	CSUD017	Sudbury reef	377	0.0003786	ch3	Asus	0.04551	2023-04-20	2024-06-20	good/good	36	27	466.0976	0.1764646	0.994

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.34 1.90
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start

## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 

## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.start.row+125, # custom 
              by = "row") 

## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.

inspect(cycle1_data)

## inspect: Applying column default of 'time = 1'

## inspect: Applying column default of 'oxygen = 2'

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 11 12 13 14 16 17 18 19 20 21 22
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.34 1.53
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()

## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##     rep rank intercept_b0    slope_b1       rsq density row endrow    time
##  1:  NA    1     375.9149 -0.04101672 0.9920842      NA  51     95 6803.90
##  2:  NA    2     375.5724 -0.04096668 0.9920467      NA  50     94 6802.53
##  3:  NA    3     374.8510 -0.04086107 0.9920132      NA  49     93 6801.16
##  4:  NA    4     374.6425 -0.04083050 0.9919451      NA  52     96 6805.25
##  5:  NA    5     373.6104 -0.04067939 0.9920164      NA  48     92 6799.75
## ---                                                                       
## 78:  NA   78     288.5257 -0.02826400 0.9568845      NA  78    122 6840.93
## 79:  NA   79     286.2281 -0.02793047 0.9591202      NA  79    123 6842.33
## 80:  NA   80     282.9713 -0.02745752 0.9629111      NA  80    124 6843.70
## 81:  NA   81     279.0731 -0.02689137 0.9677195      NA  81    125 6845.05
## 82:  NA   82     275.1252 -0.02631792 0.9718693      NA  82    126 6846.40
##     endtime    oxy endoxy        rate
##  1: 6863.90 96.834 94.380 -0.04101672
##  2: 6862.53 96.886 94.414 -0.04096668
##  3: 6861.16 96.929 94.449 -0.04086107
##  4: 6865.25 96.829 94.378 -0.04083050
##  5: 6859.75 96.965 94.498 -0.04067939
## ---                                  
## 78: 6900.93 95.377 93.439 -0.02826400
## 79: 6902.33 95.332 93.392 -0.02793047
## 80: 6903.70 95.318 93.380 -0.02745752
## 81: 6905.05 95.277 93.376 -0.02689137
## 82: 6906.40 95.195 93.377 -0.02631792
## 
## Regressions : 82 | Results : 82 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj

## adjust_rate: Rate adjustments applied using "mean" method.

## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 82 adjusted rate(s):
## Rate          : -0.04101672
## Adjustment    : -0.002117048
## Adjusted Rate : -0.03889967 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)

## select_rate: Selecting rates with rsq values between 0.93 and 1...

## ----- Selection complete. 0 rate(s) removed, 82 rate(s) remaining -----

## select_rate: Selecting highest 1 *absolute* rate values...

## ----- Selection complete. 81 rate(s) removed, 1 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow   time
## 1:  NA    1     375.9149 -0.04101672 0.9920842      NA  51     95 6803.9
##    endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1:  6863.9 96.834  94.38 -0.04101672 -0.002117048   -0.03889967 -0.03889967
##    oxy.unit time.unit  volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04551 0.0003786   NA 36 27 1.013253 -0.4137303
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:    -1092.79          NA  mgO2/hr/kg    -1092.79
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass,
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
         True_resting="") 
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest	Max_kg	Max	rsqmax	AAS_kg	AAS	Notes	True_resting
67	6	CSUD013	CSUD017	Sudbury reef	377	0.0003786	ch3	Asus	0.04551	2023-04-20	2024-06-20	good/good	36	27	466.0976	0.1764646	0.994	1092.79	0.4137303	0.9920842	626.6922	0.2372657
### Expor	ting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 

## Rows: 213 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 

write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

7

Enter specimen data

Replicate = 7 
mass = 0.0010030 
chamber = "ch2" 
Swim = "good/good"
chamber_vol = chamber2_asus
system1 = "Asus"
Notes="low, but maybe good?"

##--- time of trail ---## 
experiment_mmr_date_asus <- "20 April 2023 01 11PM/Oxygen"
experiment_mmr_date2_asus <- "20 April 2023 01 11PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 

## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre

## [1] 7.281552e-05

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post2 <- post_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post3 <- post_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 

## [1] -5.482834e-05

Resting metabolic rate

Data manipulation

firesting2_asus <- firesting_asus |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_asus, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.34 3.77
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME) 
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME) 
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])  

apoly_insp <- firesting2_asus |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)

## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 21
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.34 1.66
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=245, 
                              by="time", 
                              plot=TRUE) 

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...

## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")

## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.

## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve). 
## Ensure this is correct. The 'linear' adjustment has been performed regardless.

## adjust_rate: Rate adjustments applied using "linear" method.

apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)

## select_rate: Object contains both negative and positive rates. Ensure the chosen `method` is appropriate.

## select_rate: Selecting rates with rsq values between 0.95 and 1...

## ----- Selection complete. 2 rate(s) removed, 19 rate(s) remaining -----

## select_rate: Selecting lowest 6 *absolute* rate values...

## ----- Selection complete. 13 rate(s) removed, 6 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:  15    1     541.2166 -0.02824294 0.994      NA 5543   5722 15687.34
## 2:  17    1     569.9545 -0.02806896 0.996      NA 6332   6511 16767.97
## 3:  18    1     574.7598 -0.02750178 0.998      NA 6726   6905 17307.62
## 4:  19    1     553.8694 -0.02549564 0.988      NA 7120   7299 17847.73
## 5:  20    1     573.7880 -0.02580606 0.995      NA 7514   7693 18387.33
## 6:  21    1     639.3489 -0.02853868 0.997      NA 7909   8088 18928.19
##     endtime    oxy endoxy        rate    adjustment rate.adjusted  rate.input
## 1: 15932.41 97.764 90.957 -0.02824294 -8.383132e-05   -0.02815911 -0.02815911
## 2: 17013.43 99.363 92.781 -0.02806896 -1.008782e-04   -0.02796808 -0.02796808
## 3: 17552.71 98.666 92.100 -0.02750178 -1.093867e-04   -0.02739239 -0.02739239
## 4: 18092.88 98.642 92.240 -0.02549564 -1.179059e-04   -0.02537773 -0.02537773
## 5: 18632.47 99.256 92.918 -0.02580606 -1.264165e-04   -0.02567964 -0.02567964
## 6: 19173.62 99.151 92.224 -0.02853868 -1.349493e-04   -0.02840373 -0.02840373
##    oxy.unit time.unit  volume     mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04573 0.001003   NA 36 27 1.013253 -0.3009433
## 2:     %Air       sec 0.04573 0.001003   NA 36 27 1.013253 -0.2989017
## 3:     %Air       sec 0.04573 0.001003   NA 36 27 1.013253 -0.2927491
## 4:     %Air       sec 0.04573 0.001003   NA 36 27 1.013253 -0.2712180
## 5:     %Air       sec 0.04573 0.001003   NA 36 27 1.013253 -0.2744445
## 6:     %Air       sec 0.04573 0.001003   NA 36 27 1.013253 -0.3035576
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -300.0431          NA  mgO2/hr/kg   -300.0431
## 2:   -298.0077          NA  mgO2/hr/kg   -298.0077
## 3:   -291.8735          NA  mgO2/hr/kg   -291.8735
## 4:   -270.4068          NA  mgO2/hr/kg   -270.4068
## 5:   -273.6237          NA  mgO2/hr/kg   -273.6237
## 6:   -302.6496          NA  mgO2/hr/kg   -302.6496
## -----------------------------------------

## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest
67	7	CSUD013	CSUD017	Sudbury reef	377	0.001003	ch2	Asus	0.04573	2023-04-20	2024-06-20	good/good	36	27	293.2395	0.2941192	0.996

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.34 1.90
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start

## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 

## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 

## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.

inspect(cycle1_data)

## inspect: Applying column default of 'time = 1'

## inspect: Applying column default of 'oxygen = 2'

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  7  9 10 11 12 13 14 15 16 17 19 20 21 22 23
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.34 1.90
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()

## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     737.6538 -0.08654415 0.9979698      NA  19     63 7391.30
##   2:  NA    2     736.6932 -0.08641578 0.9977616      NA  18     62 7389.92
##   3:  NA    3     735.9956 -0.08631968 0.9976180      NA  20     64 7392.66
##   4:  NA    4     733.5820 -0.08599784 0.9969021      NA  17     61 7388.52
##   5:  NA    5     732.1001 -0.08579399 0.9968459      NA  21     65 7394.02
##  ---                                                                       
## 171:  NA  171     312.5326 -0.02990730 0.9275965      NA 120    164 7530.24
## 172:  NA  172     312.1145 -0.02986027 0.9274131      NA 124    168 7535.73
## 173:  NA  173     309.3658 -0.02949038 0.9342241      NA 121    165 7531.59
## 174:  NA  174     308.4130 -0.02936891 0.9366696      NA 123    167 7534.34
## 175:  NA  175     307.8931 -0.02929790 0.9382346      NA 122    166 7532.95
##      endtime    oxy endoxy        rate
##   1: 7451.30 97.802 92.933 -0.08654415
##   2: 7449.92 97.791 93.035 -0.08641578
##   3: 7452.66 97.774 92.899 -0.08631968
##   4: 7448.52 97.785 93.134 -0.08599784
##   5: 7454.02 97.734 92.853 -0.08579399
##  ---                                  
## 171: 7590.24 87.701 85.430 -0.02990730
## 172: 7595.73 87.347 84.859 -0.02986027
## 173: 7591.59 87.683 85.267 -0.02949038
## 174: 7594.34 87.516 84.990 -0.02936891
## 175: 7592.95 87.629 85.109 -0.02929790
## 
## Regressions : 175 | Results : 175 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj

## adjust_rate: Rate adjustments applied using "mean" method.

## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 175 adjusted rate(s):
## Rate          : -0.08654415
## Adjustment    : 7.281552e-05
## Adjusted Rate : -0.08661697 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)

## select_rate: Selecting rates with rsq values between 0.93 and 1...

## ----- Selection complete. 26 rate(s) removed, 149 rate(s) remaining -----

## select_rate: Selecting highest 1 *absolute* rate values...

## ----- Selection complete. 148 rate(s) removed, 1 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow   time
## 1:  NA    1     737.6538 -0.08654415 0.9979698      NA  19     63 7391.3
##    endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1:  7451.3 97.802 92.933 -0.08654415 7.281552e-05   -0.08661697 -0.08661697
##    oxy.unit time.unit  volume     mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04573 0.001003   NA 36 27 1.013253 -0.9256966
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -922.9278          NA  mgO2/hr/kg   -922.9278
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass,
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
         True_resting="") 
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest	Max_kg	Max	rsqmax	AAS_kg	AAS	Notes	True_resting
67	7	CSUD013	CSUD017	Sudbury reef	377	0.001003	ch2	Asus	0.04573	2023-04-20	2024-06-20	good/good	36	27	293.2395	0.2941192	0.996	922.9278	0.9256966	0.9979698	629.6883	0.6315773	low, but maybe good?
### Expor	ting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 

## Rows: 214 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 

write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

8

Enter specimen data

Replicate = 8 
mass = 0.0006911 
chamber = "ch1" 
Swim = "good/good"
chamber_vol = chamber1_asus
system1 = "Asus"
Notes="maybe"

##--- time of trail ---## 
experiment_mmr_date_asus <- "20 April 2023 01 23PM/Oxygen"
experiment_mmr_date2_asus <- "20 April 2023 01 23PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 

## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre

## [1] -0.0006515501

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post2 <- post_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post3 <- post_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 

## [1] -0.002919273

Resting metabolic rate

Data manipulation

firesting2_asus <- firesting_asus |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_asus, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.34 3.77
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME) 
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME) 
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])  

apoly_insp <- firesting2_asus |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)

## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 21
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.34 1.66
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=245, 
                              by="time", 
                              plot=TRUE) 

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...

## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")

## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.

## adjust_rate: Rate adjustments applied using "linear" method.

apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)

## select_rate: Selecting rates with rsq values between 0.95 and 1...

## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----

## select_rate: Selecting lowest 6 *absolute* rate values...

## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:  10    1     376.8071 -0.02137246 0.988      NA 3573   3752 12987.26
## 2:  15    1     431.5203 -0.02119268 0.989      NA 5543   5722 15687.34
## 3:  18    1     466.1266 -0.02119392 0.991      NA 6726   6905 17307.62
## 4:  19    1     435.7357 -0.01885982 0.976      NA 7120   7299 17847.73
## 5:  20    1     468.3746 -0.02006123 0.993      NA 7514   7693 18387.33
## 6:  21    1     510.8662 -0.02174983 0.991      NA 7909   8088 18928.19
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 13232.75 98.805 93.759 -0.02137246 -0.002678015   -0.01869444 -0.01869444
## 2: 15932.41 98.812 93.628 -0.02119268 -0.003434541   -0.01775814 -0.01775814
## 3: 17552.71 98.940 93.932 -0.02119392 -0.003888559   -0.01730536 -0.01730536
## 4: 18092.88 98.831 94.156 -0.01885982 -0.004039910   -0.01481991 -0.01481991
## 5: 18632.47 99.180 94.223 -0.02006123 -0.004191109   -0.01587012 -0.01587012
## 6: 19173.62 98.860 93.572 -0.02174983 -0.004342703   -0.01740713 -0.01740713
##    oxy.unit time.unit  volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04565 0.0006911   NA 36 27 1.013253 -0.1994425
## 2:     %Air       sec 0.04565 0.0006911   NA 36 27 1.013253 -0.1894535
## 3:     %Air       sec 0.04565 0.0006911   NA 36 27 1.013253 -0.1846230
## 4:     %Air       sec 0.04565 0.0006911   NA 36 27 1.013253 -0.1581069
## 5:     %Air       sec 0.04565 0.0006911   NA 36 27 1.013253 -0.1693111
## 6:     %Air       sec 0.04565 0.0006911   NA 36 27 1.013253 -0.1857088
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -288.5871          NA  mgO2/hr/kg   -288.5871
## 2:   -274.1333          NA  mgO2/hr/kg   -274.1333
## 3:   -267.1437          NA  mgO2/hr/kg   -267.1437
## 4:   -228.7758          NA  mgO2/hr/kg   -228.7758
## 5:   -244.9879          NA  mgO2/hr/kg   -244.9879
## 6:   -268.7148          NA  mgO2/hr/kg   -268.7148
## -----------------------------------------

## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest
67	8	CSUD013	CSUD017	Sudbury reef	377	0.0006911	ch1	Asus	0.04565	2023-04-20	2024-06-20	good/good	36	27	268.7134	0.1857078	0.9904

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.34 3.77
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start

## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 

## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 

## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.

inspect(cycle1_data)

## inspect: Applying column default of 'time = 1'

## inspect: Applying column default of 'oxygen = 2'

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.34 1.48
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()

## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     503.8589 -0.04978517 0.9978478      NA  73    117 8181.52
##   2:  NA    2     503.8522 -0.04978410 0.9978464      NA  74    118 8182.88
##   3:  NA    3     503.3759 -0.04972675 0.9977262      NA  72    116 8180.13
##   4:  NA    4     502.6602 -0.04963894 0.9977992      NA  75    119 8184.23
##   5:  NA    5     502.5293 -0.04962403 0.9975212      NA  71    115 8178.76
##  ---                                                                       
## 171:  NA  171     325.0346 -0.02812494 0.9872336      NA 137    181 8269.14
## 172:  NA  172     323.4283 -0.02793251 0.9898347      NA 141    185 8274.63
## 173:  NA  173     322.1373 -0.02777633 0.9893244      NA 138    182 8270.54
## 174:  NA  174     321.6250 -0.02771506 0.9896917      NA 139    183 8271.90
## 175:  NA  175     321.5361 -0.02770464 0.9897302      NA 140    184 8273.29
##      endtime    oxy endoxy        rate
##   1: 8241.52 96.468 93.614 -0.04978517
##   2: 8242.88 96.447 93.532 -0.04978410
##   3: 8240.13 96.524 93.669 -0.04972675
##   4: 8244.23 96.447 93.464 -0.04963894
##   5: 8238.76 96.596 93.693 -0.04962403
##  ---                                  
## 171: 8329.14 92.670 90.762 -0.02812494
## 172: 8334.63 92.254 90.571 -0.02793251
## 173: 8330.54 92.587 90.737 -0.02777633
## 174: 8331.90 92.474 90.640 -0.02771506
## 175: 8333.29 92.393 90.616 -0.02770464
## 
## Regressions : 175 | Results : 175 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj

## adjust_rate: Rate adjustments applied using "mean" method.

## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 175 adjusted rate(s):
## Rate          : -0.04978517
## Adjustment    : -0.0006515501
## Adjusted Rate : -0.04913362 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)

## select_rate: Selecting rates with rsq values between 0.93 and 1...

## ----- Selection complete. 14 rate(s) removed, 161 rate(s) remaining -----

## select_rate: Selecting highest 1 *absolute* rate values...

## ----- Selection complete. 160 rate(s) removed, 1 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1     503.8589 -0.04978517 0.9978478      NA  73    117 8181.52
##    endtime    oxy endoxy        rate    adjustment rate.adjusted  rate.input
## 1: 8241.52 96.468 93.614 -0.04978517 -0.0006515501   -0.04913362 -0.04913362
##    oxy.unit time.unit  volume      mass area  S  t        P   rate.abs
## 1:     %Air       sec 0.04565 0.0006911   NA 36 27 1.013253 -0.5241843
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -758.4782          NA  mgO2/hr/kg   -758.4782
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass,
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
         True_resting="") 
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest	Max_kg	Max	rsqmax	AAS_kg	AAS	Notes	True_resting
67	8	CSUD013	CSUD017	Sudbury reef	377	0.0006911	ch1	Asus	0.04565	2023-04-20	2024-06-20	good/good	36	27	268.7134	0.1857078	0.9904	758.4782	0.5241843	0.9978478	489.7649	0.3384765	maybe
### Expor	ting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 

## Rows: 215 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 

write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)